san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = duration_min_out)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = duration_min_out2)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Duration between 1.36 min and 71.2 min = NORMAL (90% of reponses)") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = nas_out)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Number of NAs between 5 and 58 = NORMAL") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

plot_ly(data = san, x = ~nas_pct, y = ~duration, color = ~duration_min_out2) %>% 
  layout(title = "Duration (min) by NAs by response (%)", 
         yaxis = list(title = "Duration (min)"),
         xaxis = list(title = "Percentage of NA values"))
san %>% 
  filter(!is.na(zipcode)) %>% 
  ggplot() +
  geom_boxplot(aes(y = nas_pct, x = duration_min_out)) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("NA dist. by duration in minutes") +
  xlab("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  filter(!is.na(zipcode)) %>% 
  ggplot() +
  geom_boxplot(aes(y = nas_pct, x = duration_min_out, fill = as.factor(zipcode))) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("NA dist. by duration in minutes by zip code") +
  xlab("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  filter(!is.na(zipcode), duration < 900) %>% 
  ggplot() +
  geom_boxplot(aes(y = duration, fill = as.factor(zipcode), x = nas_out)) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("Duration (min) by zip code and NA distribution") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

  1. Locate “odd responses” based on duration and number of NA in each response
  2. Duration: a) Keep responses in (2.5%, 97.5%), b) responses in (5%, 95%)
  3. NAs: a) Responses in (10%, 90%)

Part I.

view(dfSummary(san[, 5:173], plain.ascii = F, graph.magnif = .75, labels.col = T, max.string.width = 15), method = "render")

Data Frame Summary

san

Dimensions: 1013 x 169
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 Progress [numeric] Mean (sd) : 91.1 (26.8) min < med < max: 0 < 100 < 100 IQR (CV) : 0 (0.3) 35 distinct values 1013 (100.0%) 0 (0.0%)
2 Duration (in seconds) [numeric] Mean (sd) : 1746.6 (3597.5) min < med < max: 10 < 1219 < 82589 IQR (CV) : 1220 (2.1) 841 distinct values 1013 (100.0%) 0 (0.0%)
3 Finished [numeric] Min : 0 Mean : 0.9 Max : 1
0:110(10.9%)
1:903(89.1%)
 1013 (100.0%) 0 (0.0%)
4 RecordedDate [POSIXct, POSIXt] min : 2021-02-22 14:32:03 med : 2021-02-23 12:42:57 max : 2021-03-04 02:05:00 range : 9d 11H 32M 57S 984 distinct values 1013 (100.0%) 0 (0.0%)
5 ResponseId [character] 1. R_01F3YkgcZF3LT 2. R_0Ak2tVjB09xye 3. R_0Au0Xh2TGsWoJ 4. R_0BXTlGwZGOnMe 5. R_0c60ihjCKT6Rb 6. R_0CFUnzIgwaZ1t 7. R_0fxfBNmeY6qlJ 8. R_0HXvwzj23fxTW 9. R_0ilrJc7CA6LRe 10. R_0IpsG07KZKdcO [ 1003 others ]
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1003(99.0%)
 1013 (100.0%) 0 (0.0%)
6 LocationLatitude [numeric] Mean (sd) : 33.6 (5.2) min < med < max: 21.4 < 32.9 < 61.6 IQR (CV) : 7.8 (0.2) 431 distinct values 903 (89.1%) 110 (10.9%)
7 LocationLongitude [numeric] Mean (sd) : -92.6 (44.6) min < med < max: -158 < -98.5 < 117.3 IQR (CV) : 21.4 (-0.5) 432 distinct values 903 (89.1%) 110 (10.9%)
8 DistributionChannel [character] 1. anonymous
1013(100.0%)
 1013 (100.0%) 0 (0.0%)
9 UserLanguage [character] 1. EN 2. ES
1012(99.9%)
1(0.1%)
 1013 (100.0%) 0 (0.0%)
10 consent [numeric] Min : 1 Mean : 1 Max : 2
1:1012(99.9%)
2:1(0.1%)
 1013 (100.0%) 0 (0.0%)
11 disagree [numeric] 1 distinct value
1:1(100.0%)
 1 (0.1%) 1012 (99.9%)
12 zipcode [numeric] Mean (sd) : 1.5 (0.6) min < med < max: 1 < 1 < 3 IQR (CV) : 1 (0.4)
1:543(55.7%)
2:369(37.8%)
3:63(6.5%)
 975 (96.2%) 38 (3.8%)
13 per_care_1 [numeric] Mean (sd) : 7.9 (1.7) min < med < max: 0.6 < 8 < 20.5 IQR (CV) : 1.1 (0.2) 98 distinct values 896 (88.5%) 117 (11.5%)
14 per_care_2 [numeric] Mean (sd) : 1.9 (7.1) min < med < max: -99 < 1.9 < 17.9 IQR (CV) : 1.4 (3.7) 92 distinct values 896 (88.5%) 117 (11.5%)
15 per_care_3 [numeric] Mean (sd) : -0.5 (15.8) min < med < max: -99 < 1.5 < 21.4 IQR (CV) : 1 (-30.5) 87 distinct values 896 (88.5%) 117 (11.5%)
16 per_care_4 [numeric] Mean (sd) : 0 (13.5) min < med < max: -99 < 1 < 17.8 IQR (CV) : 0.9 (-477.6) 83 distinct values 896 (88.5%) 117 (11.5%)
17 per_care_wknd_1 [numeric] Min : 4 Mean : 4.6 Max : 5
4:394(44.2%)
5:498(55.8%)
 892 (88.1%) 121 (11.9%)
18 per_care_wknd_2 [numeric] Min : 4 Mean : 4.5 Max : 5
4:435(48.9%)
5:454(51.1%)
 889 (87.8%) 124 (12.2%)
19 per_care_wknd_3 [numeric] Min : 4 Mean : 4.5 Max : 5
4:472(54.2%)
5:399(45.8%)
 871 (86.0%) 142 (14.0%)
20 per_care_wknd_4 [numeric] Min : 4 Mean : 4.4 Max : 5
4:522(59.6%)
5:354(40.4%)
 876 (86.5%) 137 (13.5%)
21 pc_wknd_time_1 [numeric] Mean (sd) : 9 (5.3) min < med < max: -99 < 9 < 19 IQR (CV) : 1.9 (0.6) 86 distinct values 498 (49.2%) 515 (50.8%)
22 pc_wknd_time_2 [numeric] Mean (sd) : 3.5 (5.6) min < med < max: -99 < 2.8 < 19.1 IQR (CV) : 2.5 (1.6) 96 distinct values 453 (44.7%) 560 (55.3%)
23 pc_wknd_time_3 [numeric] Mean (sd) : 2.3 (7.6) min < med < max: -99 < 2 < 20.6 IQR (CV) : 1.5 (3.3) 86 distinct values 399 (39.4%) 614 (60.6%)
24 pc_wknd_time_4 [numeric] Mean (sd) : 2.1 (6) min < med < max: -99 < 1.5 < 19.7 IQR (CV) : 1.7 (2.9) 73 distinct values 353 (34.8%) 660 (65.2%)
25 per_care_covid_1 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 5 IQR (CV) : 1 (0.3)
1:32(3.6%)
2:63(7.1%)
3:364(41.0%)
4:251(28.3%)
5:177(20.0%)
 887 (87.6%) 126 (12.4%)
26 per_care_covid_2 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 5 IQR (CV) : 1 (0.2)
1:2(0.2%)
2:87(9.8%)
3:424(47.8%)
4:254(28.6%)
5:120(13.5%)
 887 (87.6%) 126 (12.4%)
27 per_care_covid_3 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:10(1.1%)
2:70(7.9%)
3:445(50.2%)
4:233(26.3%)
5:124(14.0%)
6:5(0.6%)
 887 (87.6%) 126 (12.4%)
28 per_care_covid_4 [numeric] Mean (sd) : 3.3 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:17(1.9%)
2:94(10.6%)
3:459(51.7%)
4:225(25.4%)
5:88(9.9%)
6:4(0.5%)
 887 (87.6%) 126 (12.4%)
29 st_act_1 [numeric] Mean (sd) : -19.5 (44.6) min < med < max: -99 < 3.8 < 18.4 IQR (CV) : 6.6 (-2.3) 121 distinct values 886 (87.5%) 127 (12.5%)
30 st_act_2 [numeric] Mean (sd) : -26 (45.9) min < med < max: -99 < 1.2 < 20.7 IQR (CV) : 101.5 (-1.8) 99 distinct values 886 (87.5%) 127 (12.5%)
31 st_act_3 [numeric] Mean (sd) : -30.9 (47.6) min < med < max: -99 < 0.7 < 21.4 IQR (CV) : 100.5 (-1.5) 92 distinct values 886 (87.5%) 127 (12.5%)
32 st_act_wknd_1 [numeric] Min : 3 Mean : 3.5 Max : 4
3:336(49.3%)
4:346(50.7%)
 682 (67.3%) 331 (32.7%)
33 st_act_wknd_2 [numeric] Min : 3 Mean : 3.5 Max : 4
3:297(46.3%)
4:344(53.7%)
 641 (63.3%) 372 (36.7%)
34 st_act_wknd_3 [numeric] Min : 3 Mean : 3.5 Max : 4
3:304(50.8%)
4:294(49.2%)
 598 (59.0%) 415 (41.0%)
35 sa_wknd_time_1 [numeric] Mean (sd) : -0.7 (20.3) min < med < max: -99 < 2.5 < 13.3 IQR (CV) : 2.3 (-27.4) 83 distinct values 347 (34.3%) 666 (65.7%)
36 sa_wknd_time_2 [numeric] Mean (sd) : 2.2 (12.8) min < med < max: -99 < 2.2 < 19 IQR (CV) : 3.7 (5.8) 97 distinct values 344 (34.0%) 669 (66.0%)
37 sa_wknd_time_3 [numeric] Mean (sd) : -13.8 (37.7) min < med < max: -99 < 1 < 16.4 IQR (CV) : 2.8 (-2.7) 73 distinct values 296 (29.2%) 717 (70.8%)
38 st_act_covid_1 [numeric] Mean (sd) : 3.6 (1.5) min < med < max: 1 < 3 < 6 IQR (CV) : 2 (0.4)
1:44(5.0%)
2:158(17.9%)
3:307(34.7%)
4:139(15.7%)
5:83(9.4%)
6:153(17.3%)
 884 (87.3%) 129 (12.7%)
39 st_act_covid_2 [numeric] Mean (sd) : 3.7 (1.4) min < med < max: 1 < 3 < 6 IQR (CV) : 2 (0.4)
1:30(3.4%)
2:144(16.3%)
3:287(32.5%)
4:182(20.6%)
5:79(8.9%)
6:162(18.3%)
 884 (87.3%) 129 (12.7%)
40 st_act_covid_3 [numeric] Mean (sd) : 3.5 (1.7) min < med < max: 1 < 3 < 6 IQR (CV) : 3 (0.5)
1:114(12.9%)
2:129(14.6%)
3:294(33.3%)
4:106(12.0%)
5:41(4.6%)
6:200(22.6%)
 884 (87.3%) 129 (12.7%)
41 own_device [numeric] Mean (sd) : 2.7 (0.7) min < med < max: 1 < 3 < 4 IQR (CV) : 0 (0.2)
1:97(11.0%)
2:51(5.8%)
3:719(81.3%)
4:17(1.9%)
 884 (87.3%) 129 (12.7%)
42 dev_act_1 [numeric] Mean (sd) : -2 (22.1) min < med < max: -99 < 2 < 16.8 IQR (CV) : 2.5 (-11.1) 110 distinct values 861 (85.0%) 152 (15.0%)
43 dev_act_7 [numeric] Mean (sd) : -0.2 (18.3) min < med < max: -99 < 2 < 18.6 IQR (CV) : 2.9 (-80.3) 109 distinct values 861 (85.0%) 152 (15.0%)
44 dev_act_3 [numeric] Mean (sd) : -13.2 (35.9) min < med < max: -99 < 1 < 20.8 IQR (CV) : 1.2 (-2.7) 84 distinct values 861 (85.0%) 152 (15.0%)
45 dev_act_6 [numeric] Mean (sd) : -11.1 (34.6) min < med < max: -99 < 1.3 < 20.9 IQR (CV) : 2.2 (-3.1) 102 distinct values 861 (85.0%) 152 (15.0%)
46 dev_act_wknd_1 [numeric] Min : 2 Mean : 2.4 Max : 3
2:466(56.9%)
3:353(43.1%)
 819 (80.8%) 194 (19.2%)
47 dev_act_wknd_2 [numeric] Min : 2 Mean : 2.4 Max : 3
2:484(58.1%)
3:349(41.9%)
 833 (82.2%) 180 (17.8%)
48 dev_act_wknd_3 [numeric] Min : 2 Mean : 2.5 Max : 3
2:339(46.2%)
3:395(53.8%)
 734 (72.5%) 279 (27.5%)
49 dev_act_wknd_4 [numeric] Min : 2 Mean : 2.4 Max : 3
2:460(61.5%)
3:288(38.5%)
 748 (73.8%) 265 (26.2%)
50 da_wknd_time_1 [numeric] Mean (sd) : 3.3 (5.1) min < med < max: -99 < 3 < 11.6 IQR (CV) : 1.9 (1.6) 83 distinct values 465 (45.9%) 548 (54.1%)
51 da_wknd_time_2 [numeric] Mean (sd) : 3.4 (5.4) min < med < max: -99 < 2.9 < 17.8 IQR (CV) : 2.5 (1.6) 102 distinct values 483 (47.7%) 530 (52.3%)
52 da_wknd_time_3 [numeric] Mean (sd) : 1.5 (11.3) min < med < max: -99 < 1.9 < 20.3 IQR (CV) : 2.1 (7.6) 81 distinct values 339 (33.5%) 674 (66.5%)
53 da_wknd_time_6 [numeric] Mean (sd) : 2.2 (12) min < med < max: -99 < 2.7 < 18 IQR (CV) : 2.4 (5.4) 99 distinct values 459 (45.3%) 554 (54.7%)
54 dev_act_covid_1 [numeric] Mean (sd) : 3.7 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:25(2.9%)
2:65(7.6%)
3:287(33.5%)
4:297(34.7%)
5:168(19.6%)
6:14(1.6%)
 856 (84.5%) 157 (15.5%)
55 dev_act_covid_2 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:13(1.5%)
2:118(13.8%)
3:269(31.4%)
4:311(36.3%)
5:136(15.9%)
6:9(1.1%)
 856 (84.5%) 157 (15.5%)
56 dev_act_covid_3 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:18(2.1%)
2:93(10.9%)
3:362(42.3%)
4:249(29.1%)
5:109(12.7%)
6:25(2.9%)
 856 (84.5%) 157 (15.5%)
57 dev_act_covid_4 [numeric] Mean (sd) : 3.6 (0.9) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:9(1.1%)
2:69(8.1%)
3:348(40.7%)
4:294(34.3%)
5:130(15.2%)
6:6(0.7%)
 856 (84.5%) 157 (15.5%)
58 dev_act_covid_5 [numeric] Mean (sd) : 3.6 (0.9) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.2)
1:12(1.4%)
2:56(6.5%)
3:314(36.7%)
4:323(37.7%)
5:145(16.9%)
6:6(0.7%)
 856 (84.5%) 157 (15.5%)
59 dev_act_covid_6 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:9(1.1%)
2:88(10.3%)
3:352(41.1%)
4:274(32.0%)
5:124(14.5%)
6:9(1.1%)
 856 (84.5%) 157 (15.5%)
60 dev_act_covid_7 [numeric] Mean (sd) : 3.8 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:16(1.9%)
2:52(6.1%)
3:252(29.4%)
4:333(38.9%)
5:162(18.9%)
6:41(4.8%)
 856 (84.5%) 157 (15.5%)
61 other_act_1 [numeric] Mean (sd) : -11.7 (35.7) min < med < max: -99 < 1.4 < 18.4 IQR (CV) : 2.4 (-3) 104 distinct values 871 (86.0%) 142 (14.0%)
62 other_act_2 [numeric] Mean (sd) : -2.8 (21.2) min < med < max: -99 < 1.1 < 15 IQR (CV) : 1.3 (-7.5) 81 distinct values 871 (86.0%) 142 (14.0%)
63 other_act_3 [numeric] Mean (sd) : -2.6 (20.4) min < med < max: -99 < 1.1 < 20.9 IQR (CV) : 1.1 (-8) 81 distinct values 871 (86.0%) 142 (14.0%)
64 other_act_4 [numeric] Mean (sd) : -24.1 (44.1) min < med < max: -99 < 0.7 < 16.8 IQR (CV) : 100.5 (-1.8) 84 distinct values 871 (86.0%) 142 (14.0%)
65 other_act_wknd_1 [numeric] Min : 2 Mean : 2.6 Max : 3
2:313(41.8%)
3:435(58.2%)
 748 (73.8%) 265 (26.2%)
66 other_act_wknd_2 [numeric] Min : 2 Mean : 2.4 Max : 3
2:481(57.8%)
3:351(42.2%)
 832 (82.1%) 181 (17.9%)
67 other_act_wknd_3 [numeric] Min : 2 Mean : 2.5 Max : 3
2:398(47.7%)
3:437(52.3%)
 835 (82.4%) 178 (17.6%)
68 other_act_wknd_4 [numeric] Min : 2 Mean : 2.5 Max : 3
2:316(48.5%)
3:335(51.5%)
 651 (64.3%) 362 (35.7%)
69 oa_wknd_time_1 [numeric] Mean (sd) : 2.8 (7.9) min < med < max: -99 < 2.4 < 17.8 IQR (CV) : 2.5 (2.8) 102 distinct values 575 (56.8%) 438 (43.2%)
70 oa_wknd_time_2 [numeric] Mean (sd) : 1.6 (9.4) min < med < max: -99 < 2 < 22.2 IQR (CV) : 1.4 (5.9) 83 distinct values 614 (60.6%) 399 (39.4%)
71 oa_wknd_time_3 [numeric] Mean (sd) : 1.3 (9.4) min < med < max: -99 < 1.5 < 18.8 IQR (CV) : 1.3 (7.2) 82 distinct values 617 (60.9%) 396 (39.1%)
72 oa_wknd_time_4 [numeric] Mean (sd) : 0 (14.4) min < med < max: -99 < 1.2 < 20.6 IQR (CV) : 1.5 (-1026) 79 distinct values 500 (49.4%) 513 (50.6%)
73 other_act_covid_1 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:26(3.0%)
2:72(8.3%)
3:394(45.4%)
4:223(25.7%)
5:128(14.8%)
6:24(2.8%)
 867 (85.6%) 146 (14.4%)
74 other_act_covid_2 [numeric] Mean (sd) : 3.6 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:12(1.4%)
2:82(9.5%)
3:321(37.0%)
4:291(33.6%)
5:152(17.5%)
6:9(1.0%)
 867 (85.6%) 146 (14.4%)
75 other_act_covid_3 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:12(1.4%)
2:72(8.3%)
3:379(43.7%)
4:260(30.0%)
5:130(15.0%)
6:14(1.6%)
 867 (85.6%) 146 (14.4%)
76 other_act_covid_4 [numeric] Mean (sd) : 3.5 (1.1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:27(3.1%)
2:75(8.7%)
3:395(45.6%)
4:236(27.2%)
5:76(8.8%)
6:58(6.7%)
 867 (85.6%) 146 (14.4%)
77 pre_electric [numeric] Mean (sd) : 169.6 (456.9) min < med < max: 1 < 100 < 9000 IQR (CV) : 80 (2.7) 107 distinct values 864 (85.3%) 149 (14.7%)
78 post_electric [numeric] Mean (sd) : 167 (280.3) min < med < max: 2 < 110 < 4000 IQR (CV) : 92.5 (1.7) 106 distinct values 863 (85.2%) 150 (14.8%)
79 pre_gas [numeric] Mean (sd) : 214.2 (471.8) min < med < max: 0 < 80 < 8000 IQR (CV) : 150 (2.2) 100 distinct values 861 (85.0%) 152 (15.0%)
80 post_gas [numeric] Mean (sd) : 172 (497.4) min < med < max: 0 < 80 < 9000 IQR (CV) : 70 (2.9) 98 distinct values 859 (84.8%) 154 (15.2%)
81 num_vehicles [numeric] Mean (sd) : 1.5 (0.9) min < med < max: 0 < 1 < 20 IQR (CV) : 1 (0.6)
0:26(3.0%)
1:471(54.8%)
2:329(38.3%)
3:30(3.5%)
4:1(0.1%)
5:1(0.1%)
20:1(0.1%)
 859 (84.8%) 154 (15.2%)
82 pre_trans [numeric] Mean (sd) : 374.3 (587.3) min < med < max: 0 < 210 < 9000 IQR (CV) : 280 (1.6) 94 distinct values 857 (84.6%) 156 (15.4%)
83 post_trans [numeric] Mean (sd) : 215.9 (611.6) min < med < max: 0 < 100 < 8080 IQR (CV) : 100 (2.8) 92 distinct values 857 (84.6%) 156 (15.4%)
84 health [numeric] Mean (sd) : 2.3 (0.9) min < med < max: 1 < 2 < 5 IQR (CV) : 1 (0.4)
1:141(16.5%)
2:339(39.6%)
3:324(37.9%)
4:42(4.9%)
5:10(1.2%)
 856 (84.5%) 157 (15.5%)
85 mental_health_anxious [numeric] Mean (sd) : 0 (14.6) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-1244.3)
-99:18(2.1%)
1:248(29.0%)
2:308(36.0%)
3:216(25.3%)
4:65(7.6%)
 855 (84.4%) 158 (15.6%)
86 mental_health_worry [numeric] Mean (sd) : -0.9 (16.7) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-19.6)
-99:24(2.8%)
1:328(38.4%)
2:247(28.9%)
3:201(23.5%)
4:55(6.4%)
 855 (84.4%) 158 (15.6%)
87 mental_health_interest [numeric] Mean (sd) : -0.2 (15.3) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-66.2)
-99:20(2.3%)
1:230(26.9%)
2:325(38.0%)
3:218(25.5%)
4:62(7.3%)
 855 (84.4%) 158 (15.6%)
88 mental_health_down [numeric] Mean (sd) : -1.1 (17.4) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-16.4)
-99:26(3.0%)
1:323(37.8%)
2:231(27.0%)
3:216(25.3%)
4:59(6.9%)
 855 (84.4%) 158 (15.6%)
89 physical_health [numeric] Mean (sd) : 3 (3.4) min < med < max: 0 < 2 < 28 IQR (CV) : 5 (1.1) 17 distinct values 854 (84.3%) 159 (15.7%)
90 mental_health [numeric] Mean (sd) : 3.9 (4.8) min < med < max: 0 < 2 < 30 IQR (CV) : 5 (1.2) 23 distinct values 853 (84.2%) 160 (15.8%)
91 pre_phy_health [numeric] Mean (sd) : 1.6 (7.7) min < med < max: -99 < 2 < 3 IQR (CV) : 1 (4.9)
-99:5(0.6%)
1:76(8.9%)
2:554(64.9%)
3:218(25.6%)
 853 (84.2%) 160 (15.8%)
92 pre_mental_health [numeric] Mean (sd) : 1.4 (7.7) min < med < max: -99 < 2 < 3 IQR (CV) : 0 (5.3)
-99:5(0.6%)
1:170(19.9%)
2:473(55.5%)
3:205(24.0%)
 853 (84.2%) 160 (15.8%)
93 days_poor_health [numeric] Mean (sd) : 3.1 (4.4) min < med < max: 0 < 2 < 25 IQR (CV) : 4 (1.4) 22 distinct values 853 (84.2%) 160 (15.8%)
94 impairment [numeric] Mean (sd) : -3.4 (22) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-6.4)
-99:43(5.0%)
1:285(33.4%)
2:525(61.5%)
 853 (84.2%) 160 (15.8%)
95 impairment_hsh...103 [numeric] Mean (sd) : -5 (24.9) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-5)
-99:56(6.6%)
1:273(32.0%)
2:524(61.4%)
 853 (84.2%) 160 (15.8%)
96 num_hsh_impair [numeric] Mean (sd) : 0.6 (1) min < med < max: 0 < 0 < 12 IQR (CV) : 1 (1.7)
0:517(60.6%)
1:212(24.9%)
2:100(11.7%)
3:11(1.3%)
4:7(0.8%)
5:1(0.1%)
6:3(0.4%)
10:1(0.1%)
12:1(0.1%)
 853 (84.2%) 160 (15.8%)
97 major_impairment [numeric] Mean (sd) : -7.1 (38) min < med < max: -99 < 7 < 14 IQR (CV) : 11 (-5.3) 15 distinct values 284 (28.0%) 729 (72.0%)
98 other_major_imp [numeric] 1 distinct value
0:1(100.0%)
 1 (0.1%) 1012 (99.9%)
99 impairment_hsh...107 [numeric] Mean (sd) : -8 (38.7) min < med < max: -99 < 7 < 14 IQR (CV) : 11 (-4.9) 15 distinct values 271 (26.8%) 742 (73.2%)
100 other_impairment_hsh [character] 1. A mild cold mak
1(100.0%)
 1 (0.1%) 1012 (99.9%)
101 days_impairment_1 [numeric] Mean (sd) : -30.8 (50.8) min < med < max: -99 < 2 < 31 IQR (CV) : 106 (-1.6) 26 distinct values 851 (84.0%) 162 (16.0%)
102 weeks_impairment_4 [numeric] Mean (sd) : -52.9 (50.2) min < med < max: -99 < -99 < 7 IQR (CV) : 100 (-1)
-99:461(54.2%)
0:63(7.4%)
1:153(18.0%)
2:84(9.9%)
3:48(5.6%)
4:25(2.9%)
5:9(1.1%)
6:4(0.5%)
7:4(0.5%)
 851 (84.0%) 162 (16.0%)
103 months_impairment_1 [numeric] Mean (sd) : -60.1 (49.2) min < med < max: -99 < -99 < 10 IQR (CV) : 100 (-0.8) 12 distinct values 851 (84.0%) 162 (16.0%)
104 years_impairment_1 [numeric] Mean (sd) : -63.1 (48.2) min < med < max: -99 < -99 < 7 IQR (CV) : 99 (-0.8)
-99:547(64.3%)
0:95(11.2%)
1:99(11.6%)
2:58(6.8%)
3:21(2.5%)
4:15(1.8%)
5:9(1.1%)
6:3(0.4%)
7:4(0.5%)
 851 (84.0%) 162 (16.0%)
105 med_device_1 [numeric] Mean (sd) : -51.1 (49.7) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:253(29.7%)
1:157(18.4%)
 851 (84.0%) 162 (16.0%)
106 med_device_2 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:338(39.7%)
1:72(8.5%)
 851 (84.0%) 162 (16.0%)
107 med_device_3 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:350(41.1%)
1:60(7.1%)
 851 (84.0%) 162 (16.0%)
108 med_device_4 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:364(42.8%)
1:46(5.4%)
 851 (84.0%) 162 (16.0%)
109 med_device_5 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:308(36.2%)
1:102(12.0%)
 851 (84.0%) 162 (16.0%)
110 med_device_6 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:406(47.7%)
1:4(0.5%)
 851 (84.0%) 162 (16.0%)
111 med_device_7 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:313(36.8%)
1:97(11.4%)
 851 (84.0%) 162 (16.0%)
112 med_device_8 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:311(36.5%)
1:99(11.6%)
 851 (84.0%) 162 (16.0%)
113 med_device_9 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:361(42.4%)
1:49(5.8%)
 851 (84.0%) 162 (16.0%)
114 med_device_10 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:379(44.5%)
1:31(3.6%)
 851 (84.0%) 162 (16.0%)
115 med_device_11 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:401(47.1%)
1:9(1.1%)
 851 (84.0%) 162 (16.0%)
116 other_med_device [character] 1. 0 2. glasses 3. Hh 4. no 5. No 6. Portable sleep 7. ventilator / re
2(25.0%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
 8 (0.8%) 1005 (99.2%)
117 emp_status [numeric] Mean (sd) : 1.8 (1.5) min < med < max: 1 < 1 < 7 IQR (CV) : 1.8 (0.8)
1:599(70.5%)
2:38(4.5%)
3:71(8.4%)
4:95(11.2%)
5:3(0.4%)
6:19(2.2%)
7:25(2.9%)
 850 (83.9%) 163 (16.1%)
118 emp_status_partner [numeric] Mean (sd) : 2.2 (1.8) min < med < max: 1 < 1 < 7 IQR (CV) : 2 (0.8)
1:515(60.6%)
2:52(6.1%)
3:80(9.4%)
4:125(14.7%)
5:5(0.6%)
6:15(1.8%)
7:58(6.8%)
 850 (83.9%) 163 (16.1%)
119 work_home [numeric] Mean (sd) : 0.3 (17.3) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (58.1)
-99:25(2.9%)
3:568(66.9%)
4:256(30.2%)
 849 (83.8%) 164 (16.2%)
120 work_home_partner [numeric] Mean (sd) : -7.3 (28.2) min < med < max: -99 < 1 < 2 IQR (CV) : 1 (-3.9)
-99:73(8.6%)
1:489(57.6%)
2:287(33.8%)
 849 (83.8%) 164 (16.2%)
121 frontline [numeric] Mean (sd) : -0.9 (15.6) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-17.5)
-99:21(2.5%)
1:336(39.6%)
2:492(58.0%)
 849 (83.8%) 164 (16.2%)
122 frontline_partner [numeric] Mean (sd) : -6.6 (27.5) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-4.2)
-99:69(8.1%)
1:306(36.0%)
2:474(55.8%)
 849 (83.8%) 164 (16.2%)
123 hrs_work_3 [numeric] Mean (sd) : 31.8 (29.8) min < med < max: -99 < 39 < 74 IQR (CV) : 10 (0.9) 69 distinct values 848 (83.7%) 165 (16.3%)
124 hrs_work_4 [numeric] Mean (sd) : -35 (61.4) min < med < max: -99 < 0 < 80 IQR (CV) : 119 (-1.8) 65 distinct values 848 (83.7%) 165 (16.3%)
125 hrs_work_partner_3 [numeric] Mean (sd) : 18 (46.3) min < med < max: -99 < 35 < 80 IQR (CV) : 16 (2.6) 74 distinct values 848 (83.7%) 165 (16.3%)
126 hrs_work_partner_4 [numeric] Mean (sd) : -41.3 (61.9) min < med < max: -99 < -99 < 80 IQR (CV) : 116 (-1.5) 68 distinct values 848 (83.7%) 165 (16.3%)
127 min_commute_3 [numeric] Mean (sd) : 11.8 (43.5) min < med < max: -99 < 25 < 60 IQR (CV) : 20 (3.7) 59 distinct values 847 (83.6%) 166 (16.4%)
128 hrs_commute_3 [numeric] Mean (sd) : -42.7 (49.5) min < med < max: -99 < 0 < 4 IQR (CV) : 100 (-1.2)
-99:369(43.6%)
0:169(20.0%)
1:240(28.3%)
2:55(6.5%)
3:12(1.4%)
4:2(0.2%)
 847 (83.6%) 166 (16.4%)
129 min_commute_partner_3 [numeric] Mean (sd) : -0.3 (53.4) min < med < max: -99 < 21 < 60 IQR (CV) : 28.5 (-191.6) 56 distinct values 847 (83.6%) 166 (16.4%)
130 hrs_commute_partnet_3 [numeric] Mean (sd) : -49.5 (50) min < med < max: -99 < -99 < 4 IQR (CV) : 100 (-1)
-99:427(50.4%)
0:136(16.1%)
1:214(25.3%)
2:52(6.1%)
3:15(1.8%)
4:3(0.4%)
 847 (83.6%) 166 (16.4%)
131 rsn_nowork [numeric] Mean (sd) : -5.4 (27.8) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (-5.1)
-99:20(8.1%)
1:8(3.2%)
2:50(20.2%)
3:144(58.3%)
4:25(10.1%)
 247 (24.4%) 766 (75.6%)
132 rsn_nowork_partner [numeric] Mean (sd) : -15.8 (39.4) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (-2.5)
-99:60(18.3%)
1:5(1.5%)
2:76(23.2%)
3:154(47.0%)
4:33(10.1%)
 328 (32.4%) 685 (67.6%)
133 eip_1 [numeric] Mean (sd) : -4.3 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-5)
-99:41(4.8%)
0:389(46.0%)
1:416(49.2%)
 846 (83.5%) 167 (16.5%)
134 eip_2 [numeric] Mean (sd) : -4.3 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-5)
-99:41(4.8%)
0:388(45.9%)
1:417(49.3%)
 846 (83.5%) 167 (16.5%)
135 eip_3 [numeric] Mean (sd) : -4.5 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.7)
-99:41(4.8%)
0:591(69.9%)
1:214(25.3%)
 846 (83.5%) 167 (16.5%)
136 eip_4 [numeric] Mean (sd) : -4.4 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.9)
-99:41(4.8%)
0:451(53.3%)
1:354(41.8%)
 846 (83.5%) 167 (16.5%)
137 eip_5 [numeric] Mean (sd) : -4.4 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.8)
-99:41(4.8%)
0:478(56.5%)
1:327(38.7%)
 846 (83.5%) 167 (16.5%)
138 eip_6 [numeric] Mean (sd) : -4.8 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-4.4)
-99:41(4.8%)
0:794(93.9%)
1:11(1.3%)
 846 (83.5%) 167 (16.5%)
139 eip_7 [numeric] Mean (sd) : -4.8 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-4.4)
-99:41(4.8%)
0:792(93.6%)
1:13(1.5%)
 846 (83.5%) 167 (16.5%)
140 other_eip [character] 1. 会 2. 0 3. 1 4. 1989 5. 200 6. 5000 7. Courses in hear 8. hui 9. no 10. shopping
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
2(18.2%)
1(9.1%)
 11 (1.1%) 1002 (98.9%)
141 year [numeric] Mean (sd) : 17.5 (7) min < med < max: 1 < 17 < 63 IQR (CV) : 8 (0.4) 42 distinct values 846 (83.5%) 167 (16.5%)
142 gender [numeric] Mean (sd) : 1.7 (0.6) min < med < max: 1 < 2 < 6 IQR (CV) : 1 (0.3)
1:286(33.8%)
2:550(65.1%)
3:2(0.2%)
4:2(0.2%)
6:5(0.6%)
 845 (83.4%) 168 (16.6%)
143 gender_7_TEXT [numeric] 1 distinct value
-99:845(100.0%)
 845 (83.4%) 168 (16.6%)
144 hispanic [numeric] Mean (sd) : 1.5 (0.5) min < med < max: 1 < 1 < 3 IQR (CV) : 1 (0.4)
1:425(50.4%)
2:404(47.9%)
3:15(1.8%)
 844 (83.3%) 169 (16.7%)
145 race [numeric] Mean (sd) : 1.4 (1) min < med < max: 1 < 1 < 8 IQR (CV) : 0 (0.7)
1:681(80.7%)
2:91(10.8%)
3:14(1.7%)
4:42(5.0%)
5:6(0.7%)
6:5(0.6%)
7:1(0.1%)
8:4(0.5%)
 844 (83.3%) 169 (16.7%)
146 education [numeric] Mean (sd) : 4.1 (6.3) min < med < max: -99 < 4 < 7 IQR (CV) : 3 (1.5)
-99:3(0.4%)
1:10(1.2%)
2:43(5.1%)
3:183(21.7%)
4:216(25.6%)
5:144(17.1%)
6:199(23.6%)
7:46(5.5%)
 844 (83.3%) 169 (16.7%)
147 marital [numeric] Mean (sd) : 2 (0.6) min < med < max: 1 < 2 < 6 IQR (CV) : 0 (0.3)
1:78(9.2%)
2:695(82.3%)
3:55(6.5%)
4:4(0.5%)
5:4(0.5%)
6:8(0.9%)
 844 (83.3%) 169 (16.7%)
148 depen [numeric] Min : 1 Mean : 1.2 Max : 2
1:650(77.0%)
2:194(23.0%)
 844 (83.3%) 169 (16.7%)
149 num_hsh [numeric] Mean (sd) : 3.7 (1.6) min < med < max: 0 < 4 < 10 IQR (CV) : 2 (0.4) 11 distinct values 844 (83.3%) 169 (16.7%)
150 num_children [numeric] Mean (sd) : 1.2 (1.7) min < med < max: 0 < 1 < 18 IQR (CV) : 0 (1.4) 11 distinct values 844 (83.3%) 169 (16.7%)
151 num_retired [numeric] Mean (sd) : 0.7 (0.9) min < med < max: 0 < 0 < 6 IQR (CV) : 1 (1.2)
0:447(53.0%)
1:196(23.2%)
2:189(22.4%)
3:4(0.5%)
4:5(0.6%)
5:2(0.2%)
6:1(0.1%)
 844 (83.3%) 169 (16.7%)
152 num_rooms [numeric] Mean (sd) : 4.4 (1.8) min < med < max: 0 < 4 < 22 IQR (CV) : 2 (0.4) 12 distinct values 844 (83.3%) 169 (16.7%)
153 num_bedrooms [numeric] Mean (sd) : 2.9 (1) min < med < max: 1 < 3 < 9 IQR (CV) : 1 (0.3)
1:42(5.0%)
2:237(28.1%)
3:359(42.6%)
4:145(17.2%)
5:49(5.8%)
6:8(1.0%)
7:1(0.1%)
9:1(0.1%)
 842 (83.1%) 171 (16.9%)
154 sq_ft [numeric] Mean (sd) : 5.3 (2.8) min < med < max: 1 < 5 < 13 IQR (CV) : 4 (0.5) 13 distinct values 844 (83.3%) 169 (16.7%)
155 hardships_1 [numeric] Mean (sd) : -11.4 (32.5) min < med < max: -99 < 1 < 1 IQR (CV) : 1 (-2.9)
-99:102(12.1%)
0:250(29.6%)
1:492(58.3%)
 844 (83.3%) 169 (16.7%)
156 hardships_2 [numeric] Mean (sd) : -11.8 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:587(69.5%)
1:155(18.4%)
 844 (83.3%) 169 (16.7%)
157 hardships_3 [numeric] Mean (sd) : -11.7 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.8)
-99:102(12.1%)
0:539(63.9%)
1:203(24.1%)
 844 (83.3%) 169 (16.7%)
158 hardships_4 [numeric] Mean (sd) : -11.7 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.8)
-99:102(12.1%)
0:532(63.0%)
1:210(24.9%)
 844 (83.3%) 169 (16.7%)
159 hardships_5 [numeric] Mean (sd) : -11.8 (32.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:610(72.3%)
1:132(15.6%)
 844 (83.3%) 169 (16.7%)
160 hardships_6 [numeric] Mean (sd) : -11.9 (32.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:669(79.3%)
1:73(8.6%)
 844 (83.3%) 169 (16.7%)
161 welfare_1 [numeric] Mean (sd) : -9.2 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:470(55.7%)
1:293(34.7%)
 844 (83.3%) 169 (16.7%)
162 welfare_2 [numeric] Mean (sd) : -9.2 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:509(60.3%)
1:254(30.1%)
 844 (83.3%) 169 (16.7%)
163 welfare_3 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:644(76.3%)
1:119(14.1%)
 844 (83.3%) 169 (16.7%)
164 welfare_4 [numeric] Mean (sd) : -9.1 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:410(48.6%)
1:353(41.8%)
 844 (83.3%) 169 (16.7%)
165 welfare_5 [numeric] Mean (sd) : -9.3 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:589(69.8%)
1:174(20.6%)
 844 (83.3%) 169 (16.7%)
166 welfare_6 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:673(79.7%)
1:90(10.7%)
 844 (83.3%) 169 (16.7%)
167 welfare_7 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:675(80.0%)
1:88(10.4%)
 844 (83.3%) 169 (16.7%)
168 email [character] Emails Valid Invalid Duplicates
839(99.9%)
1(0.1%)
34(4.0%)
 840 (82.9%) 173 (17.1%)
169 email_confirm [numeric] 1 distinct value
1:840(100.0%)
 840 (82.9%) 173 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.4)
2021-03-22

Part II.

Which responses to keep?

  1. Consider odd response those outside the “normal” range of duration (1.36, 71) AND above 91 NA values
  2. Summary table: responses that Agree to participate, valid zip code (78202 or 78230), not an odd response
### Duration and number of NAs
kable(table(san$nas_out, san$duration_min_out), caption = "NAs (%) vs Duration (min)") %>% 
  kable_classic(full_width = F) %>% 
  footnote(general = "NAs in (10%, 90%); duration in (2.5%, 97.5%)")
NAs (%) vs Duration (min)
Spend too much time Normal Spend too little time
Above 85% quantile 8 111 33
Normal 15 672 0
Below 10% quantile 3 171 0
Note:
NAs in (10%, 90%); duration in (2.5%, 97.5%) 
kable(table(san$nas_out, san$duration_min_out2), caption = "NAs (%) vs Duration (min)") %>% 
  kable_classic(full_width = F) %>% 
  footnote(general = "NAs in (10%, 90%); duration in (5%, 95%)")
NAs (%) vs Duration (min)
Above 95% quantile Normal Below 5% quantile
Above 85% quantile 10 91 51
Normal 37 650 0
Below 10% quantile 4 170 0
Note:
NAs in (10%, 90%); duration in (5%, 95%) 
kable(table(san$odd_r2), caption = "Odd responses") %>% 
  kable_classic(full_width = F)
Odd responses
Var1 Freq
Normal 952
Odd response 61 
san2 <- san %>% 
  # consent Agree and double check on survey participation. Keep zip code 78202 or 78230. Keep the first answer for unique emails
  filter(consent == "1" | disagree == "1", zipcode != 3, odd_r2 == "Normal") %>% 
  mutate(zipcode = if_else(zipcode == "1", "Elm Creek", 
                           if_else(zipcode == "2", "Jefferson Heights", NA_character_)),
         rep_email = if_else(email == lag(email, n = 1, order_by = email), "rep_email", "first_entry")) %>% 
  # Keep only the first response for those repeated emails 
  filter(rep_email == "first_entry")

#view(dfSummary(san2[, 5:173], plain.ascii = F, graph.magnif = .75, labels.col = T, max.string.width = 15), method = "render")

Section IV. Background Characteristics by Zip code

san2 %>%
  select(16, 146, 148:171, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section IV. Background characteristics (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
gender 805 >0.9
1 164 (34%) 112 (34%)
2 310 (65%) 210 (64%)
3 1 (0.2%) 1 (0.3%)
4 1 (0.2%) 1 (0.3%)
6 3 (0.6%) 2 (0.6%)
hispanic 805 0.4
1 235 (49%) 174 (53%)
2 234 (49%) 147 (45%)
3 10 (2.1%) 5 (1.5%)
race 805 0.013
1 384 (80%) 263 (81%)
2 58 (12%) 32 (9.8%)
3 9 (1.9%) 4 (1.2%)
4 20 (4.2%) 19 (5.8%)
5 0 (0%) 6 (1.8%)
6 5 (1.0%) 0 (0%)
7 0 (0%) 1 (0.3%)
8 3 (0.6%) 1 (0.3%)
education 805
-99 3 (0.6%) 0 (0%)
1 7 (1.5%) 3 (0.9%)
2 24 (5.0%) 15 (4.6%)
3 108 (23%) 67 (21%)
4 120 (25%) 88 (27%)
5 78 (16%) 59 (18%)
6 118 (25%) 73 (22%)
7 21 (4.4%) 21 (6.4%)
marital 805 0.7
1 46 (9.6%) 30 (9.2%)
2 391 (82%) 269 (83%)
3 31 (6.5%) 22 (6.7%)
4 3 (0.6%) 1 (0.3%)
5 4 (0.8%) 0 (0%)
6 4 (0.8%) 4 (1.2%)
depen 805 0.8
1 367 (77%) 247 (76%)
2 112 (23%) 79 (24%)
num_hsh 805 3.7 (1.5) 3.8 (1.6) 0.6
num_children 805 1.2 (1.5) 1.3 (2.1) 0.5
num_retired 805 0.050
0 277 (58%) 159 (49%)
1 108 (23%) 81 (25%)
2 86 (18%) 83 (25%)
3 3 (0.6%) 1 (0.3%)
4 3 (0.6%) 2 (0.6%)
5 2 (0.4%) 0 (0%)
num_rooms 805 4.2 (1.6) 4.5 (1.7) 0.028
num_bedrooms 803 0.6
1 27 (5.7%) 14 (4.3%)
2 137 (29%) 92 (28%)
3 196 (41%) 149 (46%)
4 84 (18%) 50 (15%)
5 29 (6.1%) 16 (4.9%)
6 3 (0.6%) 4 (1.2%)
7 0 (0%) 1 (0.3%)
9 1 (0.2%) 0 (0%)
Unknown 2 0
sq_ft 805 5.2 (2.8) 5.2 (2.7) >0.9
hardships_1 805 0.7
-99 56 (12%) 45 (14%)
0 146 (30%) 96 (29%)
1 277 (58%) 185 (57%)
hardships_2 805 0.6
-99 56 (12%) 45 (14%)
0 335 (70%) 219 (67%)
1 88 (18%) 62 (19%)
hardships_3 805 0.3
-99 56 (12%) 45 (14%)
0 317 (66%) 198 (61%)
1 106 (22%) 83 (25%)
hardships_4 805 0.2
-99 56 (12%) 45 (14%)
0 309 (65%) 191 (59%)
1 114 (24%) 90 (28%)
hardships_5 805 0.6
-99 56 (12%) 45 (14%)
0 349 (73%) 228 (70%)
1 74 (15%) 53 (16%)
hardships_6 805 0.12
-99 56 (12%) 45 (14%)
0 387 (81%) 245 (75%)
1 36 (7.5%) 36 (11%)
welfare_1 805 0.5
-99 52 (11%) 28 (8.6%)
0 275 (57%) 186 (57%)
1 152 (32%) 112 (34%)
welfare_2 805 0.6
-99 52 (11%) 28 (8.6%)
0 282 (59%) 197 (60%)
1 145 (30%) 101 (31%)
welfare_3 805 0.056
-99 52 (11%) 28 (8.6%)
0 373 (78%) 243 (75%)
1 54 (11%) 55 (17%)
welfare_4 805 0.15
-99 52 (11%) 28 (8.6%)
0 237 (49%) 147 (45%)
1 190 (40%) 151 (46%)
welfare_5 805 0.5
-99 52 (11%) 28 (8.6%)
0 327 (68%) 233 (71%)
1 100 (21%) 65 (20%)
welfare_6 805 0.6
-99 52 (11%) 28 (8.6%)
0 374 (78%) 263 (81%)
1 53 (11%) 35 (11%)
welfare_7 805 0.3
-99 52 (11%) 28 (8.6%)
0 371 (77%) 268 (82%)
1 56 (12%) 30 (9.2%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 n (%); Mean (SD)

2 Fisher's exact test; Pearson's Chi-squared test; Wilcoxon rank sum test

Section Ia. Time Use by Zip code

san2 %>%
  select(16:80, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section Ia. Time Use (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
per_care_1 805 8.0 (1.8) 7.9 (1.6) 0.8
per_care_2 805 2.1 (6.9) 2.0 (5.9) 0.045
per_care_3 805 0.0 (14.7) -1.5 (18.3) 0.2
per_care_4 805 0.3 (12.3) -0.1 (13.7) 0.5
per_care_wknd_1 805 0.3
4 216 (45%) 136 (42%)
5 263 (55%) 190 (58%)
per_care_wknd_2 803 0.064
4 251 (53%) 149 (46%)
5 227 (47%) 176 (54%)
Unknown 1 1
per_care_wknd_3 785 0.6
4 260 (55%) 169 (53%)
5 209 (45%) 147 (47%)
Unknown 10 10
per_care_wknd_4 792 0.3
4 290 (61%) 185 (58%)
5 182 (39%) 135 (42%)
Unknown 7 6
pc_wknd_time_1 453 9.1 (2.1) 9.5 (1.9) 0.11
Unknown 216 136
pc_wknd_time_2 403 3.9 (3.1) 3.6 (2.8) 0.3
Unknown 252 150
pc_wknd_time_3 356 2.5 (7.6) 2.4 (2.0) 0.037
Unknown 270 179
pc_wknd_time_4 317 2.4 (2.7) 2.4 (2.7) >0.9
Unknown 297 191
per_care_covid_1 805 0.2
1 22 (4.6%) 8 (2.5%)
2 32 (6.7%) 26 (8.0%)
3 182 (38%) 143 (44%)
4 137 (29%) 88 (27%)
5 106 (22%) 61 (19%)
per_care_covid_2 805 0.5
1 1 (0.2%) 1 (0.3%)
2 50 (10%) 30 (9.2%)
3 226 (47%) 153 (47%)
4 133 (28%) 105 (32%)
5 69 (14%) 37 (11%)
per_care_covid_3 805 >0.9
1 6 (1.3%) 3 (0.9%)
2 37 (7.7%) 23 (7.1%)
3 242 (51%) 165 (51%)
4 124 (26%) 87 (27%)
5 68 (14%) 45 (14%)
6 2 (0.4%) 3 (0.9%)
per_care_covid_4 805 >0.9
1 10 (2.1%) 7 (2.1%)
2 53 (11%) 33 (10%)
3 238 (50%) 172 (53%)
4 128 (27%) 81 (25%)
5 48 (10%) 31 (9.5%)
6 2 (0.4%) 2 (0.6%)
st_act_1 805 -15.9 (42.3) -24.1 (47.1) 0.015
st_act_2 805 -23.8 (44.8) -29.1 (47.3) 0.016
st_act_3 805 -26.3 (45.7) -37.0 (49.4) 0.001
st_act_wknd_1 622 0.4
3 185 (48%) 123 (52%)
4 199 (52%) 115 (48%)
Unknown 95 88
st_act_wknd_2 583 0.4
3 164 (46%) 113 (50%)
4 192 (54%) 114 (50%)
Unknown 123 99
st_act_wknd_3 547 0.2
3 183 (53%) 97 (48%)
4 161 (47%) 106 (52%)
Unknown 135 123
sa_wknd_time_1 315 0.5 (17.7) -3.0 (24.7) 0.3
Unknown 279 211
sa_wknd_time_2 306 3.3 (8.3) 1.5 (13.8) 0.2
Unknown 287 212
sa_wknd_time_3 269 -9.4 (32.9) -18.8 (42.3) 0.9
Unknown 317 219
st_act_covid_1 805 0.15
1 23 (4.8%) 16 (4.9%)
2 94 (20%) 53 (16%)
3 165 (34%) 115 (35%)
4 75 (16%) 46 (14%)
5 50 (10%) 25 (7.7%)
6 72 (15%) 71 (22%)
st_act_covid_2 805 0.001
1 15 (3.1%) 11 (3.4%)
2 86 (18%) 52 (16%)
3 166 (35%) 86 (26%)
4 87 (18%) 86 (26%)
5 48 (10%) 18 (5.5%)
6 77 (16%) 73 (22%)
st_act_covid_3 805 0.007
1 58 (12%) 46 (14%)
2 84 (18%) 40 (12%)
3 167 (35%) 97 (30%)
4 62 (13%) 36 (11%)
5 22 (4.6%) 14 (4.3%)
6 86 (18%) 93 (29%)
own_device 805 0.3
1 49 (10%) 43 (13%)
2 27 (5.6%) 18 (5.5%)
3 394 (82%) 263 (81%)
4 9 (1.9%) 2 (0.6%)
dev_act_1 794 -2.2 (22.6) -1.8 (21.6) 0.6
Unknown 9 2
dev_act_7 794 0.3 (17.0) 0.2 (17.0) 0.8
Unknown 9 2
dev_act_3 794 -11.0 (33.8) -16.3 (38.7) 0.10
Unknown 9 2
dev_act_6 794 -9.7 (33.0) -13.7 (37.4) >0.9
Unknown 9 2
dev_act_wknd_1 755 0.3
2 264 (59%) 171 (55%)
3 182 (41%) 138 (45%)
Unknown 33 17
dev_act_wknd_2 772 0.7
2 267 (58%) 179 (57%)
3 190 (42%) 136 (43%)
Unknown 22 11
dev_act_wknd_3 677 0.3
2 180 (44%) 128 (48%)
3 230 (56%) 139 (52%)
Unknown 69 59
dev_act_wknd_4 687 0.3
2 249 (60%) 174 (64%)
3 165 (40%) 99 (36%)
Unknown 65 53
da_wknd_time_1 435 3.6 (1.8) 3.0 (8.1) 0.11
Unknown 215 155
da_wknd_time_2 446 3.3 (6.9) 3.6 (2.4) 0.8
Unknown 212 147
da_wknd_time_3 308 1.8 (11.1) 1.8 (9.4) 0.15
Unknown 299 198
da_wknd_time_6 423 1.4 (14.7) 3.7 (2.7) 0.042
Unknown 230 152
dev_act_covid_1 794 0.5
1 11 (2.3%) 11 (3.4%)
2 36 (7.7%) 25 (7.7%)
3 148 (31%) 115 (35%)
4 160 (34%) 112 (35%)
5 105 (22%) 57 (18%)
6 10 (2.1%) 4 (1.2%)
Unknown 9 2
dev_act_covid_2 794 0.3
1 8 (1.7%) 2 (0.6%)
2 58 (12%) 49 (15%)
3 148 (31%) 102 (31%)
4 176 (37%) 118 (36%)
5 72 (15%) 52 (16%)
6 8 (1.7%) 1 (0.3%)
Unknown 9 2
dev_act_covid_3 794 0.6
1 10 (2.1%) 7 (2.2%)
2 53 (11%) 31 (9.6%)
3 186 (40%) 147 (45%)
4 149 (32%) 87 (27%)
5 57 (12%) 42 (13%)
6 15 (3.2%) 10 (3.1%)
Unknown 9 2
dev_act_covid_4 794 >0.9
1 5 (1.1%) 2 (0.6%)
2 36 (7.7%) 26 (8.0%)
3 191 (41%) 131 (40%)
4 163 (35%) 115 (35%)
5 72 (15%) 47 (15%)
6 3 (0.6%) 3 (0.9%)
Unknown 9 2
dev_act_covid_5 794 0.5
1 8 (1.7%) 3 (0.9%)
2 31 (6.6%) 20 (6.2%)
3 165 (35%) 127 (39%)
4 190 (40%) 113 (35%)
5 72 (15%) 59 (18%)
6 4 (0.9%) 2 (0.6%)
Unknown 9 2
dev_act_covid_6 794 0.6
1 5 (1.1%) 4 (1.2%)
2 44 (9.4%) 39 (12%)
3 203 (43%) 126 (39%)
4 151 (32%) 100 (31%)
5 62 (13%) 51 (16%)
6 5 (1.1%) 4 (1.2%)
Unknown 9 2
dev_act_covid_7 794 0.027
1 6 (1.3%) 9 (2.8%)
2 31 (6.6%) 19 (5.9%)
3 145 (31%) 87 (27%)
4 196 (42%) 116 (36%)
5 70 (15%) 75 (23%)
6 22 (4.7%) 18 (5.6%)
Unknown 9 2
other_act_1 805 -12.1 (36.1) -11.7 (35.5) 0.7
other_act_2 805 -3.6 (23.0) -2.2 (19.8) 0.8
other_act_3 805 -2.6 (20.8) -2.6 (20.5) 0.5
other_act_4 805 -25.1 (44.7) -23.0 (43.4) 0.6
other_act_wknd_1 690 >0.9
2 171 (42%) 118 (42%)
3 239 (58%) 162 (58%)
Unknown 69 46
other_act_wknd_2 767 >0.9
2 261 (57%) 180 (58%)
3 193 (43%) 133 (42%)
Unknown 25 13
other_act_wknd_3 770 0.4
2 220 (48%) 140 (45%)
3 238 (52%) 172 (55%)
Unknown 21 14
other_act_wknd_4 601 0.3
2 176 (50%) 112 (45%)
3 178 (50%) 135 (55%)
Unknown 125 79
oa_wknd_time_1 532 2.9 (6.4) 2.4 (10.2) >0.9
Unknown 163 110
oa_wknd_time_2 567 1.6 (9.8) 2.0 (7.1) >0.9
Unknown 139 99
oa_wknd_time_3 570 0.9 (11.1) 2.3 (2.4) 0.030
Unknown 138 97
oa_wknd_time_4 463 -0.2 (15.0) 0.4 (12.9) 0.7
Unknown 204 138
other_act_covid_1 805 0.064
1 12 (2.5%) 13 (4.0%)
2 41 (8.6%) 29 (8.9%)
3 199 (42%) 164 (50%)
4 137 (29%) 67 (21%)
5 75 (16%) 45 (14%)
6 15 (3.1%) 8 (2.5%)
other_act_covid_2 805 0.4
1 5 (1.0%) 7 (2.1%)
2 49 (10%) 27 (8.3%)
3 170 (35%) 124 (38%)
4 158 (33%) 116 (36%)
5 90 (19%) 50 (15%)
6 7 (1.5%) 2 (0.6%)
other_act_covid_3 805 0.4
1 10 (2.1%) 2 (0.6%)
2 44 (9.2%) 21 (6.4%)
3 198 (41%) 149 (46%)
4 147 (31%) 101 (31%)
5 71 (15%) 48 (15%)
6 9 (1.9%) 5 (1.5%)
other_act_covid_4 805 0.3
1 12 (2.5%) 14 (4.3%)
2 45 (9.4%) 24 (7.4%)
3 206 (43%) 154 (47%)
4 133 (28%) 87 (27%)
5 44 (9.2%) 29 (8.9%)
6 39 (8.1%) 18 (5.5%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 Mean (SD); n (%)

2 Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test

Section Ib. Energy Use by Zip code

san2 %>%
  select(16, 81:87, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section Ib. Energy Use (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
pre_electric 805 172.2 (408.8) 173.8 (553.8) 0.3
post_electric 805 170.3 (304.2) 163.8 (265.3) 0.4
pre_gas 805 214.5 (382.2) 214.9 (587.1) 0.016
post_gas 805 164.3 (423.6) 185.6 (601.5) 0.6
num_vehicles 805 0.2
0 14 (2.9%) 12 (3.7%)
1 264 (55%) 167 (51%)
2 188 (39%) 130 (40%)
3 12 (2.5%) 16 (4.9%)
5 1 (0.2%) 0 (0%)
20 0 (0%) 1 (0.3%)
pre_trans 805 335.3 (483.0) 421.0 (726.2) 0.042
post_trans 805 198.2 (557.8) 255.5 (705.8) 0.051
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 Mean (SD); n (%)

2 Wilcoxon rank sum test; Fisher's exact test

Section II. Health Conditions by Zip code

san2 %>%
  select(16, 88:101, 103, 105:119, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section II. Health Conditions (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
health 805 0.5
1 81 (17%) 49 (15%)
2 203 (42%) 123 (38%)
3 167 (35%) 131 (40%)
4 23 (4.8%) 18 (5.5%)
5 5 (1.0%) 5 (1.5%)
mental_health_anxious 805 0.002
-99 3 (0.6%) 15 (4.6%)
1 142 (30%) 92 (28%)
2 177 (37%) 115 (35%)
3 115 (24%) 85 (26%)
4 42 (8.8%) 19 (5.8%)
mental_health_worry 805 0.004
-99 6 (1.3%) 18 (5.5%)
1 185 (39%) 125 (38%)
2 142 (30%) 93 (29%)
3 109 (23%) 76 (23%)
4 37 (7.7%) 14 (4.3%)
mental_health_interest 805 0.002
-99 4 (0.8%) 16 (4.9%)
1 140 (29%) 79 (24%)
2 173 (36%) 133 (41%)
3 124 (26%) 75 (23%)
4 38 (7.9%) 23 (7.1%)
mental_health_down 805 0.028
-99 8 (1.7%) 18 (5.5%)
1 185 (39%) 123 (38%)
2 134 (28%) 81 (25%)
3 114 (24%) 84 (26%)
4 38 (7.9%) 20 (6.1%)
physical_health 805 3.2 (3.6) 2.9 (3.1) 0.7
mental_health 805 4.1 (5.0) 3.7 (4.4) 0.8
pre_phy_health 805 >0.9
-99 3 (0.6%) 2 (0.6%)
1 46 (9.6%) 28 (8.6%)
2 308 (64%) 207 (63%)
3 122 (25%) 89 (27%)
pre_mental_health 805 0.3
-99 1 (0.2%) 4 (1.2%)
1 94 (20%) 62 (19%)
2 270 (56%) 177 (54%)
3 114 (24%) 83 (25%)
days_poor_health 805 3.2 (4.5) 3.0 (4.3) 0.8
impairment 805 0.5
-99 27 (5.6%) 14 (4.3%)
1 161 (34%) 103 (32%)
2 291 (61%) 209 (64%)
impairment_hsh...103 805 0.6
-99 30 (6.3%) 24 (7.4%)
1 155 (32%) 96 (29%)
2 294 (61%) 206 (63%)
num_hsh_impair 805 0.5
0 281 (59%) 207 (63%)
1 123 (26%) 81 (25%)
2 61 (13%) 31 (9.5%)
3 7 (1.5%) 4 (1.2%)
4 5 (1.0%) 1 (0.3%)
5 1 (0.2%) 0 (0%)
6 1 (0.2%) 1 (0.3%)
10 0 (0%) 1 (0.3%)
major_impairment 264 -7.4 (38.7) -6.7 (37.1) 0.5
Unknown 318 223
impairment_hsh...107 251 -6.5 (37.0) -10.5 (41.5) 0.7
Unknown 324 230
days_impairment_1 805 -30.6 (50.8) -32.0 (51.0) 0.8
weeks_impairment_4 805
-99 263 (55%) 172 (53%)
0 40 (8.4%) 20 (6.1%)
1 74 (15%) 68 (21%)
2 48 (10%) 32 (9.8%)
3 30 (6.3%) 17 (5.2%)
4 11 (2.3%) 13 (4.0%)
5 8 (1.7%) 1 (0.3%)
6 2 (0.4%) 2 (0.6%)
7 3 (0.6%) 1 (0.3%)
months_impairment_1 805 -59.1 (49.4) -60.2 (49.3) >0.9
years_impairment_1 805
-99 303 (63%) 210 (64%)
0 61 (13%) 28 (8.6%)
1 53 (11%) 42 (13%)
2 30 (6.3%) 26 (8.0%)
3 11 (2.3%) 10 (3.1%)
4 10 (2.1%) 5 (1.5%)
5 6 (1.3%) 3 (0.9%)
6 2 (0.4%) 1 (0.3%)
7 3 (0.6%) 1 (0.3%)
med_device_1 805 0.2
-99 255 (53%) 158 (48%)
0 129 (27%) 108 (33%)
1 95 (20%) 60 (18%)
med_device_2 805 0.2
-99 255 (53%) 158 (48%)
0 188 (39%) 132 (40%)
1 36 (7.5%) 36 (11%)
med_device_3 805 0.4
-99 255 (53%) 158 (48%)
0 192 (40%) 142 (44%)
1 32 (6.7%) 26 (8.0%)
med_device_4 805 0.3
-99 255 (53%) 158 (48%)
0 196 (41%) 151 (46%)
1 28 (5.8%) 17 (5.2%)
med_device_5 805 0.4
-99 255 (53%) 158 (48%)
0 172 (36%) 126 (39%)
1 52 (11%) 42 (13%)
med_device_6 805 0.4
-99 255 (53%) 158 (48%)
0 223 (47%) 167 (51%)
1 1 (0.2%) 1 (0.3%)
med_device_7 805 0.3
-99 255 (53%) 158 (48%)
0 180 (38%) 130 (40%)
1 44 (9.2%) 38 (12%)
med_device_8 805 0.2
-99 255 (53%) 158 (48%)
0 174 (36%) 123 (38%)
1 50 (10%) 45 (14%)
med_device_9 805 0.2
-99 255 (53%) 158 (48%)
0 203 (42%) 146 (45%)
1 21 (4.4%) 22 (6.7%)
med_device_10 805 0.4
-99 255 (53%) 158 (48%)
0 208 (43%) 154 (47%)
1 16 (3.3%) 14 (4.3%)
med_device_11 805 0.4
-99 255 (53%) 158 (48%)
0 219 (46%) 165 (51%)
1 5 (1.0%) 3 (0.9%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 n (%); Mean (SD)

2 Fisher's exact test; Pearson's Chi-squared test; Wilcoxon rank sum test

Section III. Employment by Zip code

san2 %>%
  select(16, 121:143, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section III. Employment (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
emp_status 805
1 336 (70%) 234 (72%)
2 25 (5.2%) 12 (3.7%)
3 48 (10%) 21 (6.4%)
4 46 (9.6%) 41 (13%)
5 1 (0.2%) 2 (0.6%)
6 9 (1.9%) 9 (2.8%)
7 14 (2.9%) 7 (2.1%)
emp_status_partner 805
1 270 (56%) 226 (69%)
2 39 (8.1%) 11 (3.4%)
3 58 (12%) 14 (4.3%)
4 68 (14%) 44 (13%)
5 3 (0.6%) 2 (0.6%)
6 9 (1.9%) 6 (1.8%)
7 32 (6.7%) 23 (7.1%)
work_home 805 0.3
-99 11 (2.3%) 12 (3.7%)
3 332 (69%) 212 (65%)
4 136 (28%) 102 (31%)
work_home_partner 805 0.9
-99 39 (8.1%) 30 (9.2%)
1 279 (58%) 188 (58%)
2 161 (34%) 108 (33%)
frontline 805 0.054
-99 11 (2.3%) 8 (2.5%)
1 176 (37%) 147 (45%)
2 292 (61%) 171 (52%)
frontline_partner 805 0.2
-99 36 (7.5%) 28 (8.6%)
1 163 (34%) 130 (40%)
2 280 (58%) 168 (52%)
hrs_work_3 805 32.3 (28.1) 30.5 (33.0) 0.6
hrs_work_4 805 -29.9 (61.5) -38.8 (61.3) 0.059
hrs_work_partner_3 805 16.8 (47.1) 21.1 (44.0) 0.4
hrs_work_partner_4 805 -36.2 (63.1) -44.7 (60.6) 0.038
min_commute_3 805 10.3 (44.1) 11.6 (43.4) 0.7
hrs_commute_3 805 0.3
-99 203 (42%) 146 (45%)
0 109 (23%) 53 (16%)
1 128 (27%) 98 (30%)
2 32 (6.7%) 22 (6.7%)
3 6 (1.3%) 6 (1.8%)
4 1 (0.2%) 1 (0.3%)
min_commute_partner_3 805 -2.6 (54.0) 3.1 (51.2) 0.2
hrs_commute_partnet_3 805 0.11
-99 251 (52%) 154 (47%)
0 84 (18%) 47 (14%)
1 102 (21%) 98 (30%)
2 32 (6.7%) 19 (5.8%)
3 8 (1.7%) 7 (2.1%)
4 2 (0.4%) 1 (0.3%)
rsn_nowork 232 0.9
-99 12 (8.5%) 8 (8.9%)
1 4 (2.8%) 4 (4.4%)
2 32 (23%) 16 (18%)
3 80 (56%) 52 (58%)
4 14 (9.9%) 10 (11%)
Unknown 337 236
rsn_nowork_partner 304 0.2
-99 32 (16%) 25 (26%)
1 3 (1.5%) 2 (2.0%)
2 49 (24%) 23 (23%)
3 101 (49%) 42 (43%)
4 21 (10%) 6 (6.1%)
Unknown 273 228
eip_1 805 0.3
-99 21 (4.4%) 17 (5.2%)
0 214 (45%) 161 (49%)
1 244 (51%) 148 (45%)
eip_2 805 0.6
-99 21 (4.4%) 17 (5.2%)
0 227 (47%) 143 (44%)
1 231 (48%) 166 (51%)
eip_3 805 0.3
-99 21 (4.4%) 17 (5.2%)
0 345 (72%) 217 (67%)
1 113 (24%) 92 (28%)
eip_4 805 0.5
-99 21 (4.4%) 17 (5.2%)
0 264 (55%) 166 (51%)
1 194 (41%) 143 (44%)
eip_5 805 0.9
-99 21 (4.4%) 17 (5.2%)
0 269 (56%) 182 (56%)
1 189 (39%) 127 (39%)
eip_6 805 0.8
-99 21 (4.4%) 17 (5.2%)
0 451 (94%) 305 (94%)
1 7 (1.5%) 4 (1.2%)
eip_7 805 0.2
-99 21 (4.4%) 17 (5.2%)
0 453 (95%) 301 (92%)
1 5 (1.0%) 8 (2.5%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 n (%); Mean (SD)

2 Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test

Part III.

load("~/GitHub/san/sa_survey.RData")

san %>% 
  filter(health != "NA") %>% 
  ggplot(aes(x = race_eth, fill = health)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by the feeling nervous, anxious, or on edge") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA") %>% 
  ggplot(aes(x = race_eth, fill = anxious)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by the feeling nervous, anxious, or on edge") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = worry)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by not being able to stop or control worrying") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = interest)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by having little interest or pleasure in doing things") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = down)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by feeling down, depressed, or hopeless") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(pre_c19_ph != "NA", pre_c19_mh != "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = pre_c19_ph)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "D", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and physical health prior to COVID-19") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(pre_c19_ph != "NA", pre_c19_mh != "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = pre_c19_mh)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "D", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health prior to COVID-19") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))
san %>% 
  filter(front != "NA") %>% 
  ggplot(aes(x = race_eth, fill = front)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "white") +
  scale_fill_viridis_d(option = "B", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and identification as frontline worker") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(front != "NA",  gender != "NA", work_hrs > 0 ) %>% 
  ggplot() +
  geom_col(aes(x = work_hrs, y = race_eth, color = gender, fill = gender), position = "dodge", width = .5) +
  facet_grid(~ zip) +
  scale_color_viridis_d(option = "E", begin = .2, end = .8) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  ggtitle("Responses by zip code and working hours per week") +
  xlab("Hours per week") + 
  ylab(NULL) +
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))
san.ia %>% 
  select(2, 15:18, 28:30, 44:50, 63:66, 70:84) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section Ia. Time Use (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
sleep.covid 805 0.2
About the same 182 (38%) 143 (44%)
Much less time 22 (4.6%) 8 (2.5%)
Much more time 106 (22%) 61 (19%)
Somewhat less time 32 (6.7%) 26 (8.0%)
Somewhat more time 137 (29%) 88 (27%)
eat.covid 805 0.5
About the same 226 (47%) 153 (47%)
Much less time 1 (0.2%) 1 (0.3%)
Much more time 69 (14%) 37 (11%)
Somewhat less time 50 (10%) 30 (9.2%)
Somewhat more time 133 (28%) 105 (32%)
cook.covid 800 >0.9
About the same 242 (51%) 165 (51%)
Much less time 6 (1.3%) 3 (0.9%)
Much more time 68 (14%) 45 (14%)
Somewhat less time 37 (7.8%) 23 (7.1%)
Somewhat more time 124 (26%) 87 (27%)
Unknown 2 3
groom.covid 801 >0.9
About the same 238 (50%) 172 (53%)
Much less time 10 (2.1%) 7 (2.2%)
Much more time 48 (10%) 31 (9.6%)
Somewhat less time 53 (11%) 33 (10%)
Somewhat more time 128 (27%) 81 (25%)
Unknown 2 2
class.covid 662 0.7
About the same 165 (41%) 115 (45%)
Much less time 23 (5.7%) 16 (6.3%)
Much more time 50 (12%) 25 (9.8%)
Somewhat less time 94 (23%) 53 (21%)
Somewhat more time 75 (18%) 46 (18%)
Unknown 72 71
hmwrk.covid 655 0.005
About the same 166 (41%) 86 (34%)
Much less time 15 (3.7%) 11 (4.3%)
Much more time 48 (12%) 18 (7.1%)
Somewhat less time 86 (21%) 52 (21%)
Somewhat more time 87 (22%) 86 (34%)
Unknown 77 73
commute.covid 626 0.5
About the same 167 (42%) 97 (42%)
Much less time 58 (15%) 46 (20%)
Much more time 22 (5.6%) 14 (6.0%)
Somewhat less time 84 (21%) 40 (17%)
Somewhat more time 62 (16%) 36 (15%)
Unknown 86 93
tv.covid 780 0.4
About the same 148 (32%) 115 (36%)
Much less time 11 (2.4%) 11 (3.4%)
Much more time 105 (23%) 57 (18%)
Somewhat less time 36 (7.8%) 25 (7.8%)
Somewhat more time 160 (35%) 112 (35%)
Unknown 19 6
stream.covid 785 0.6
About the same 148 (32%) 102 (32%)
Much less time 8 (1.7%) 2 (0.6%)
Much more time 72 (16%) 52 (16%)
Somewhat less time 58 (13%) 49 (15%)
Somewhat more time 176 (38%) 118 (37%)
Unknown 17 3
radio.covid 769 0.4
About the same 186 (41%) 147 (47%)
Much less time 10 (2.2%) 7 (2.2%)
Much more time 57 (13%) 42 (13%)
Somewhat less time 53 (12%) 31 (9.9%)
Somewhat more time 149 (33%) 87 (28%)
Unknown 24 12
snet.covid 788 >0.9
About the same 191 (41%) 131 (41%)
Much less time 5 (1.1%) 2 (0.6%)
Much more time 72 (15%) 47 (15%)
Somewhat less time 36 (7.7%) 26 (8.1%)
Somewhat more time 163 (35%) 115 (36%)
Unknown 12 5
text.covid 788 0.4
About the same 165 (35%) 127 (39%)
Much less time 8 (1.7%) 3 (0.9%)
Much more time 72 (15%) 59 (18%)
Somewhat less time 31 (6.7%) 20 (6.2%)
Somewhat more time 190 (41%) 113 (35%)
Unknown 13 4
pc.covid 785 0.5
About the same 203 (44%) 126 (39%)
Much less time 5 (1.1%) 4 (1.2%)
Much more time 62 (13%) 51 (16%)
Somewhat less time 44 (9.5%) 39 (12%)
Somewhat more time 151 (32%) 100 (31%)
Unknown 14 6
xbox.covid 754 0.015
About the same 145 (32%) 87 (28%)
Much less time 6 (1.3%) 9 (2.9%)
Much more time 70 (16%) 75 (25%)
Somewhat less time 31 (6.9%) 19 (6.2%)
Somewhat more time 196 (44%) 116 (38%)
Unknown 31 20
read.covid 782 0.039
About the same 199 (43%) 164 (52%)
Much less time 12 (2.6%) 13 (4.1%)
Much more time 75 (16%) 45 (14%)
Somewhat less time 41 (8.8%) 29 (9.1%)
Somewhat more time 137 (30%) 67 (21%)
Unknown 15 8
clean.covid 796 0.4
About the same 170 (36%) 124 (38%)
Much less time 5 (1.1%) 7 (2.2%)
Much more time 90 (19%) 50 (15%)
Somewhat less time 49 (10%) 27 (8.3%)
Somewhat more time 158 (33%) 116 (36%)
Unknown 7 2
wash.covid 791 0.2
About the same 198 (42%) 149 (46%)
Much less time 10 (2.1%) 2 (0.6%)
Much more time 71 (15%) 48 (15%)
Somewhat less time 44 (9.4%) 21 (6.5%)
Somewhat more time 147 (31%) 101 (31%)
Unknown 9 5
repair.covid 748 0.5
About the same 206 (47%) 154 (50%)
Much less time 12 (2.7%) 14 (4.5%)
Much more time 44 (10%) 29 (9.4%)
Somewhat less time 45 (10%) 24 (7.8%)
Somewhat more time 133 (30%) 87 (28%)
Unknown 39 18
sleep.tt 805 57.3 (12.1) 57.5 (11.1) 0.8
eat.tt 802 18.7 (15.4) 17.5 (13.3) 0.2
Unknown 2 1
cook.tt 783 15.1 (16.2) 13.8 (13.3) 0.3
Unknown 11 11
groom.tt 792 13.0 (16.2) 12.3 (14.3) 0.6
Unknown 7 6
class.tt 603 36.7 (20.9) 35.9 (22.4) 0.5
Unknown 103 99
hmwrk.tt 575 20.5 (18.4) 19.3 (19.7) 0.029
Unknown 126 104
commute.tt 503 16.0 (20.5) 15.8 (19.9) 0.5
Unknown 154 148
tv.tt 754 22.5 (17.2) 21.8 (16.9) 0.3
Unknown 33 18
pc.tt 771 22.1 (17.4) 22.1 (16.3) 0.8
Unknown 23 11
radio.tt 673 13.9 (17.5) 12.6 (14.3) 0.6
Unknown 71 61
xbox.tt 681 17.8 (18.8) 19.2 (17.4) 0.069
Unknown 70 54
read.tt 687 19.8 (19.3) 18.8 (17.7) 0.5
Unknown 71 47
clean.tt 764 13.7 (15.1) 13.2 (13.2) 0.8
Unknown 27 14
wash.tt 767 12.8 (16.3) 12.4 (13.2) 0.4
Unknown 24 14
repair.tt 594 13.2 (17.4) 12.3 (15.2) >0.9
Unknown 131 80

1 n (%); Mean (SD)

2 Pearson's Chi-squared test; Fisher's exact test; Wilcoxon rank sum test 

san.ia
## # A tibble: 805 x 84
##    ResponseId zipcode sleep.t eat.t cook.t groom.t sleep.w eat.w cook.w groom.w
##    <chr>      <chr>     <dbl> <dbl>  <dbl>   <dbl> <chr>   <chr> <chr>  <chr>  
##  1 R_1mPgbz0~ Elm Cr~     9     1.5    1.3     1.3 Same t~ Same~ Same ~ Same t~
##  2 R_Td5pHI7~ Jeffer~     9     1.4    2.2     1.4 Differ~ Diff~ Diffe~ Same t~
##  3 R_uk2ixO2~ Elm Cr~     8.1   2.7    5.5     1.1 Same t~ Diff~ Diffe~ Same t~
##  4 R_2R9yFul~ Elm Cr~     8.4   0.9    1.4     1.1 Same t~ Same~ Diffe~ Differ~
##  5 R_10V2q2U~ Jeffer~     7.5   1.2    2.5     0.8 Same t~ Diff~ Diffe~ Same t~
##  6 R_Rl5erZQ~ Elm Cr~     6.9   3.7    2.3     1.9 Differ~ Same~ Same ~ Differ~
##  7 R_3s1bxu5~ Elm Cr~     7.4   2.9    3.3     1   Same t~ Same~ Same ~ Same t~
##  8 R_sHDMDHp~ Elm Cr~     7.6   1.1    2.1     2   Same t~ Same~ Same ~ Same t~
##  9 R_1cZAR33~ Jeffer~     6.4   2.2    1.5     1   Differ~ Same~ Same ~ Same t~
## 10 R_z2Jw6TJ~ Elm Cr~     7.3   2.7    0.6     1.3 Same t~ Same~ Same ~ Same t~
## # ... with 795 more rows, and 74 more variables: sleep.t.w <dbl>,
## #   eat.t.w <dbl>, cook.t.w <dbl>, groom.t.w <dbl>, sleep.covid <chr>,
## #   eat.covid <chr>, cook.covid <chr>, groom.covid <chr>, class.t <dbl>,
## #   hmwrk.t <dbl>, commute.t <dbl>, class.w <chr>, hmwrk.w <chr>,
## #   commute.w <chr>, class.t.w <dbl>, hmwrk.t.w <dbl>, commute.t.w <dbl>,
## #   class.covid <chr>, hmwrk.covid <chr>, commute.covid <chr>,
## #   own_device <chr>, tv.t <dbl>, pc.t <dbl>, radio.t <dbl>, xbox.t <dbl>,
## #   tv.w <chr>, pc.w <chr>, radio.w <chr>, xbox.w <chr>, tv.t.w <dbl>,
## #   pc.t.w <dbl>, radio.t.w <dbl>, xbox.t.w <dbl>, tv.covid <chr>,
## #   stream.covid <chr>, radio.covid <chr>, snet.covid <chr>, text.covid <chr>,
## #   pc.covid <chr>, xbox.covid <chr>, read.t <dbl>, clean.t <dbl>,
## #   wash.t <dbl>, repair.t <dbl>, read.w <chr>, clean.w <chr>, wash.w <chr>,
## #   repair.w <chr>, read.t.w <dbl>, clean.t.w <dbl>, wash.t.w <dbl>,
## #   repair.t.w <dbl>, read.covid <chr>, clean.covid <chr>, wash.covid <chr>,
## #   repair.covid <chr>, number_missing_value <dbl>, nas_pct <dbl>,
## #   duration <dbl>, sleep.tt <dbl>, eat.tt <dbl>, cook.tt <dbl>,
## #   groom.tt <dbl>, class.tt <dbl>, hmwrk.tt <dbl>, commute.tt <dbl>,
## #   tv.tt <dbl>, pc.tt <dbl>, radio.tt <dbl>, xbox.tt <dbl>, read.tt <dbl>,
## #   clean.tt <dbl>, wash.tt <dbl>, repair.tt <dbl>